DEVELOPMENT... OpenML
Run
156803

Run 156803

Task 29 (Supervised Classification) credit-approval Uploaded 11-02-2015 by Felicia West
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I10
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

22 Evaluation measures

Area under ROC curve
0.9333
Per class
F measure
0.8683
Per class
Kappa
0.7338
KB relative information score
421.4225
Mean absolute error
0.205
Mean prior absolute error
0.494
Number of instances
690
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8688
Per class
Predictive accuracy
0.8681
Prior entropy
0.9913
Recall
0.8681
Per class
Relative absolute error
0.4149
Root mean prior squared error
0.497
Root mean squared error
0.3138
Root relative squared error
0.6315
SciMark benchmark
902.4773
UserCPU time millis
20
UserCPU time millis training
20